JNET: Learning User Representations via Joint Network Embedding and Topic Embedding
This work addresses the challenge of capturing latent user intents from complex multimodal data for applications like content recommendation and expert finding, representing an incremental improvement over existing methods.
The paper tackled the problem of learning user representations from multimodal data by constructing a shared latent space to capture dependencies between social connections and text content, resulting in improved predictive power for unseen documents and link prediction compared to state-of-the-art models.
User representation learning is vital to capture diverse user preferences, while it is also challenging as user intents are latent and scattered among complex and different modalities of user-generated data, thus, not directly measurable. Inspired by the concept of user schema in social psychology, we take a new perspective to perform user representation learning by constructing a shared latent space to capture the dependency among different modalities of user-generated data. Both users and topics are embedded to the same space to encode users' social connections and text content, to facilitate joint modeling of different modalities, via a probabilistic generative framework. We evaluated the proposed solution on large collections of Yelp reviews and StackOverflow discussion posts, with their associated network structures. The proposed model outperformed several state-of-the-art topic modeling based user models with better predictive power in unseen documents, and state-of-the-art network embedding based user models with improved link prediction quality in unseen nodes. The learnt user representations are also proved to be useful in content recommendation, e.g., expert finding in StackOverflow.